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Updated: May 20, 2025

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对影响人类表皮生长因子受体2的因素的系统分析解释一致性:染色协议,基于人工智能的图像标准化和分类标准

Chen Jiang¹, Mei Li¹, Chengyou Zheng¹

¹State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.

Laboratory investigation; a journal of technical methods and pathology

|March 25, 2025

概括

标准化人类表皮生长因子受体2 (HER2) 测试可以改善乳腺癌的诊断. 人工智能模型和新的标准提高了HER2解释的一致性,有助于治疗决策.

科学领域:

在瘤学瘤学.
病理学病理学病理学
生物医学工程生物医学工程

背景情况:

精确的人体表皮生长因子受体2 (HER2) 评估对于乳腺癌治疗至关重要,特别是针对HER2向治疗.
免疫组织化学 (IHC) 染色方案的变化和观察者之间的分歧损害了HER2状态确定的可靠性.
现有的分类标准在一致解释HER2状态方面存在挑战,特别是对于低和超低表达水平.

研究的目的:

研究影响乳腺癌诊断中的HER2解释一致性的因素.
评估不同IHC染色协议和人工智能 (AI) 模型对HER2评估可靠性的影响.
为了比较现有的和新的HER2分类标准在改善观察者间协议方面的表现.

主要方法:

来自1063例乳腺癌病例的组织微阵列使用三个不同的IHC协议进行染色.
开发了一个新的AI模型来标准化HER2染色图像.
五组染色组织微阵列由八名病理学家独立审查,使用Fleiss Kappa和总体同意率测量观察者间的一致性. 后勤回归分析了诊断准确性.

主要成果:

诺迪QC协议显示了最高的观察者间协议 (Kappa 0.754).
基于人工智能的图像标准化显著改善了一致性,特别是在HER2低病例中,将结果与Nordi QC标准 (P < .001) 保持一致.
拟议的零,超低/低,积极的标准显示出比ASCO/CAP 2023标准更好的可靠性,特别是在挑战HER2超低病例 (卡帕<0.20) 时.

结论:

IHC染色协议和HER2分类标准的变化显著影响了诊断的一致性.
人工智能驱动的图像标准化提供了一种有希望的方法来提高HER2解释可靠性.
采用零,超低/低,积极的标准可以改进诊断精度,以改善乳腺癌治疗决策.

关键词:

人工智能的人工智能是人工智能.乳腺癌乳腺癌乳腺癌分类标准分类标准分类标准人类表皮生长因子受体2 免疫组织化学染色染色观察者之间的协议.

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